Data is everywhere in this age of digital transformation where smart systems rely on data for better functioning, offering insights and predictive capabilities. Artificial intelligence depends on data inputs for producing business results. Without clean data, there is no AI.
Enterprises often face cycles of irrelevant data that fails to deliver good results and this explains why over 50% of AI projects fail. Data scientists should make good decisions about data before feeding into their AI systems¹.
Not all data can solve your business problems and this comes down to data decisions made by data scientists. The #datascience team must focus on the problem and ask questions about the right data needed to solve the challenge. At this stage, the data scientist has a chance to collect clean data and improve the quality of data fed into the AI system.
Training algorithms for your machine learning models² with the wrong data produces errors and this becomes hectic for data scientists, as the process must begin again to collect clean data.
Data scientists have different approaches for controlling data inputs in their AI system and in this article; I will explore some applicable methods.
1. The Data Quality Model
The data quality model is commonplace in many organizations where data science teams develop a benchmark for data collected. These guidelines for data inputs give data scientists a picture of data they should expect during the collection process. This avoids wrong decisions and irrelevant data. It becomes easier to narrow down the kind of data you need for your ML models and creates efficiency in the process.
Quality data standards have shown to optimize machine-learning projects, reduce time wastage and ensure that the project meets required objectives. The data quality model offers data scientists a framework for exploring and reviewing data prior to feeding in their AI systems. At the same time, bad data is collected without the knowledge of data scientists and the data quality³ model pinpoints problems quickly.
2. Business Problem Control
The problem your data is trying to solve should always guide the data scientist when making decisions about data. Business use cases are critical and this means using the right #data to understand the problem and offer solutions. Some companies use extreme data for their problems and this overrides their original problem. There is no need for additional data that does not help in solving your business problem hence business problem control is a good starting point.
For instance, a retail business based in San Francisco that targets white women aged 30–45 years should not collect data about women below 25 or 30 years. Going outside the scope of the problem area is a recipe for inaccurate results. The data scientist⁴ should not lose focus of the real problem and should prepare to make tough choices about data.
3. Controlling the Source of Data
The source of data comes first when collecting data for use in machine learning models and AI systems. You need to understand the source of your data as any wrong decision could interfere with the success of your AI project⁵. Often times, data scientists fail to identify the accurate source points of their data and reviewing this decision is important for successful implementation.
Trust comes second when dealing with data sources and here you need to ascertain the direction your data is coming from. There is no problem with doubting a data source⁶ and the #datascientist can leave that option and look for more alternatives. A good example is separating feeds you use for extracting data sources. By selecting the right data feeds and leaving those not relevant, you narrow down results for more data accuracy.
4. Optimizing Algorithms
When implementing your AI project, algorithms enable the training of data and this requires refining #algorithms to achieve high accuracy. For example, when working on an ML project that requires achieving high accuracy levels, your best bet is fine-tuning the algorithms for better performance. Algorithms determine the success of an AI project and data scientists should test algorithms⁷ and continuously improve for good outcomes.
Organizations should focus on improving their algorithms for maximum benefits including accuracy. Algorithms close to accuracy levels of 100% such as 96% and 97% perform better compared to those with accuracy levels of less than 80%. This comes down to optimizing the algorithms and enables the data science team⁸ to spot mistakes and address them before proceeding to the next phase of #AI implementation.
Data Controls for Your Machine Learning Projects
Implementing the right controls for your data is the foundation of successful AI projects. However, data scientists become stuck on their own versions of the right data and miss important clues that will determine the success of their projects. A clear understanding of the data controls means making choices about what is clean data and unclean data.
It is your responsibility as a data scientist to explore and experiment data before making decisions. Develop a clear framework for your data and this checklist will help you navigate the data collection phase while ensuring that you collect data tailored to your business problem.